This data was obtained from an internal survey on student
satisfaction levels regarding service, cleanliness, and food products at
the Binus Anggrek Campus cafeteria. The survey was conducted anonymously
using a Likert scale in bahasa language.
Data Cleaning & Preprocessing
Identifies Missing Value and The Causes
## Missing values is founded in:
## major : 6 missing value(s)
## home_campus : 4 missing value(s)
The main reason for the missing values, especially for Home
Campus, is that the respondents are not active participants in the
mobility program. Since the purpose is to measure the satisfaction of
students who are actively involved in the mobility
program, I decided to remove these entries with missing
Home Campus values.
For column Jurusan, I imputed the missing values
using the most frequently occurring major. This approach is intended to
preserve useful responses, as the remaining data may still contribute
valuable insights for the analysis.
## Missing values after deletion and imputation:
## major: 0
## home_campus: 0
From the results above it shows a successful attemp to handle missing
values
Handle Inconsistent Values in Likert Scale
To anticipate inconsistent values, I’ll change all the likert
categories to a lowercase (in case there are some miss type in the
questionnaire form)
## Before mutation: Setuju, Sangat Setuju, Tidak merasakan, Sangat Tidak Setuju
## After mutation: setuju, sangat setuju, tidak merasakan, sangat tidak setuju
This process is to anticipate errors in encoding process, since the
inconsistent value is completely handled, encoding is needed to
transform the likert category to a numeric form of scale.
Separating Date, Timestamp, and Durations
By default, the tool used to gather questionnaire results has a time
stamp about starting time to a completion time. Where in this case I
will parse them to an identical column. In this case I’ll separate the
date and time into an individual column.
Encoding Likert Scale and frequency
## Unique value for every likert scale: setuju, sangat setuju, sangat tidak setuju, tidak setuju, tidak merasakan
The result above are unique values. Where in this case, every likert
scales will be encoded manually to a range number from one(1) to
five(5).
## Unique value for every likert scale (after encoding): 4, 5, 1, 2, 3
New Column for Further Analysis
Average/Mean Satisfaction Score
Here I make a new column to support further analysis and prediction
tasks. This value is calculated as the average of every encoded
likert scale. Why is this important? it provides a single
quantitative measure that summarizes overall student satisfaction. It
allows for easier comparison between respondents and can be used as a
target variable in predictive modeling or segmentation analysis.
Average/Mean Score in Every aspects
To perform an in-depth analysis of the performance of each metric
measured such as services, cleanliness, and product, I created a new
column to store the average of each question to measure respondents’
satisfaction with the aspects
Analysis
Distribution of Duration
The first analysis is to see the distribution of duration. This graph
might help to evaluate respondents who fill out the questionnaire too
quickly or too slowly, which indicates their seriousness in answering
the questionnaire.
Based on the histogram above, respondents are more likely to complete
the questionnaire in about 2 minutes. This data shows that respondents
answered the questionnaire fairly quickly.
At 0 minutes, this could be caused by some respondents answered the
questionnaire very quickly and might not read the questions (not taking
it seriously). Further analysis will be explained through the
descriptive statistics below.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.520 2.277 3.065 5.427 4.082 86.400
The shortest duration for respondent to fill questionnaire is 0.520
minutes.
Canteen Visitors Frequency
In this analysis, I’ll try to measure visitation rate with
satisfaction. In order to categorize the satisfaction level, I’ll use
discretization or binning to make a category out of mean satisfaction
score. The mapping of this process are as follows:
(MSS = Mean Satisfaction Score)
- MSS less or equal to 1 = “Bad”
- MSS greater than 1 AND MSS less or equal to 3 = “Quite Bad”
- MSS greater than 3 AND MSS less than 5 = “Good”
- MSS equal to 5 = “Excellent”
Graph View
After discretization or binning process, the analysis will be
utilizing a plot to show the Amount of Canteen Visitors by
Frequent Visiting and Satisfaction Category
Explanation:
- The visitors mostly come once until two times a week, meaning that
the canteen is not a frequent place to visit. The overall satisfaction
level is on “good” condition with 2 respond show an excellent
satisfaction level.
- Respondent who chose “0 kali” or rarely go to canteen showing the
most “quite bad” satisfaction level among others. This might indicated
by some people that had a unfulfilled expectations.
Seeing the Weaknesses of each
Aspect
In this analysis, I will focus on aspects that have a “Need
Improvements” status which referring to Product and Cleanliness aspect.
Here, I will create a descriptive statistical table to dig deeper into
the areas that are weaknesses that greatly affect the average student
satisfaction score for the Binus Anggrek cafeteria.
In this process, it’ll require binning technique to categorize each
variables based on their average scores. The Binning process will be
mapped as follows:
- mean less than 3.5 = “Needs Improvement”
- mean less than 4 = “Medium”
- mean greater than or equal 4 = “Good”
Graph View
Cleanliness
The average of cleaning_facility variable have the
lowest results such as 3,62 (if rounded by 2), followed by
neat_furniture_placement with result of the average is
3,69. These both variable are the weak point for cleanliness aspect.
This weaknesses also affecting the total average score of cleanliness
(could be found at “Analysis” tab) to be the lowest among all existing
aspects.
Both of those variable have a high standard deviation (sd > 0,5)
which indicate that the distribution was quite wide among the
respondents’ answers. Moreover, there was a change in the location of
the cafeteria during the collection of the questionnaire results, it
might affect the wide response that has mentioned before.
Product
In Product aspect, there are two different variables that fell into
“Medium” category, such as affordable_price with the
average of 3,65 and price_vs_quality with an average of
3,88. The affordable_price variable had a high standard
deviation (~0,93), this indicates a fairly wide spread among
respondents’ answers and may be influenced by various segments
available.
Services
Finally, most respondents stated that service aspects fell into the
“Good” category, as seen from the average of each variable, which was
greater than 4 (“Agree” on the Likert scale) and the consistency of
respondents (median) answering 4 on most service aspects. However,
despite the ratings falling into the “Good” category,
staff_response falls into the “Medium” category, as not all
respondents felt that the staff in the cafeteria were responsive to
questions or complaints.
Segmentation Based on Home Campus
This analysis was conducted to gain insight into the responses of
respondents from various campuses regarding three aspects that influence
student satisfaction with the Binus Anggrek cafeteria.
Explanations:
Based on home campus segmentation, the majority of students from
Binus Bandung and Binus Semarang have a satisfaction category
classified as “Good,” with a few of them falling into the
“Excellent” category. On the other hand, some respondents from
Binus Semarang fall into the “Quite Bad” review
category, which may be due to their lowered expectations regarding the
Binus Anggrek canteen. Based on the above results, although satisfaction
levels are relatively stable, there is room for improvement to
make the experience truly exceptional.
Average Score per Aspect Grouped by Home
Campus
This analysis was conducted to determine the level of satisfaction of
respondents from different home campuses with regard to the three
aspects of the cafeteria.
Graph View
Main Insight:
- The Service aspect (services_score) consistently
received the highest score across all campuses, with the highest score
belonging to Binus Malang (4.112) and the lowest to Binus Greater
Jakarta (4.000).
- Cleanliness (cleanliness_score) shows the greatest
difference between campuses:
2.1. Highest score: Binus Malang (4.008)
2.2. Lowest score: Binus Semarang (3.754), this score also
caused this campus to be the only one in the “Needs Improvement”
category in the previous analysis.
- Products (product_score) tend to be in the middle,
relatively consistent with small differences between campuses
(~0.1).
Explanation for Binus Greater Jakarta: The
graph above shows a comparison of scores for each aspect of the Binus
Anggrek cafeteria assessment, grouped by home campuses. Some of the data
above cannot be confirmed for accuracy, particularly for respondents
from the Binus Greater Jakarta campus, as there was only one respondent
who scored a 4 on all three aspects. Therefore, the results of
respondent from greater jakarta should not be used as a basis for
decision-making.
Segmentation Based on Major
This analysis was conducted to gain insight into the average
satisfaction scores of students grouped by major. Therefore I’ll check
the unique values in case there were so many majors are in the
datasets.
## [1] "digital business"
## [2] "computer science"
## [3] "public relations"
## [4] "communication - marketing communication"
## [5] "information systems"
## [6] "data science"
## [7] "computer science - software engineering"
## [8] "artificial intelligence"
## [9] "cyber security"
## [10] "game application and technology"
## [11] "interior design"
## [12] "computer engineering"
There are 12 unique values in major that might require to turn them
into a broad categories. For example:
- Tech / IT: Computer Science, Software Engineering,
AI, Cyber Security, Computer Engineering, Data Science, Information
Systems
- Design / Creative: Interior Design, Game
Application
- Business / Comm: Digital Business, Marketing Comm,
Public Relations
Graph View
Insights & Explanations:
Based on the segmentation of grouped majors, students from the Tech
field dominate the respondent population (n = 95), making the scores
from this group the most representative reference.
On the other hand, the Design and Business/Communication groups
showed relatively higher satisfaction scores. However, due to the very
small number of respondents (n = 2 and n = 5), these results are not
sufficiently robust to be generalized. This finding underscores
the importance of balanced representation in surveys to ensure
that the analysis reflects the entire student population.
Conclusions
The analysis shows that student satisfaction levels vary widely in
every aspect. The previous analysis stated that most respondents
answered the questionnaire within 2 minutes, which indirectly shows the
seriousness of the respondents in filling out the questionnaire. From
the analysis of the cafeteria assessment based on each aspect, there are
two aspects that must be given special attention, namely cleanliness and
product. Based on the analysis results, there is a significant spread in
the aspects of cleanliness and product, where students have diverse
answers based on their home campus and major category.
Summary of Key Findings
Some key points are achieved from previous
analysis that are described as follows:
- Majority of respondents are dominated by student in tech field,
showing analysis results based on the perspective of tech
students.
- Cleanliness aspect had the lowest average score compared with the
others. This showed us that cleanliness must be prioritized with such
development.
- The variation range of responses are quite big, showed by some of
the variable (e.g., cleaning_facility) had more than 0.5 (~0.96) for
standard deviation. This variation is affected due to canteen that
changes location during the survey period.
- Home campus segmentation show a significant differences between
average score. This clearly showed that student from Binus Semarang
dominates the questionnaire respond with a bit high on variability (sd =
~0.57 until ~0.67).
Prioritized Aspect for Improvement
In these three aspects (Service, Cleanliness, Product), there are
some variables that had a space for improvement which will be explained
as follows:
Cleanliness
Cleanliness aspect have the lowest average score by ~3.88 and
fell onto “Need Improvement” category. This aspect has
several variables related to it score. The following variables
fell into the “Medium” category with plenty of room for
improvement in terms of canteen performance in the cleanliness
aspect:
Prioritized Variable for Cleanliness
- cleaning_facility (“The cafeteria provides facilities such as sinks,
tissues, and adequate hand washing facilities.”)
- neat_furniture_placement (“The layout and arrangement of
tables/chairs in the cafeteria ensure orderly queues.”)
- cozy_seating_area (“The cafeteria has a comfortable and adequate
seating area.”)
- cozy_room_layout (“The layout and arrangement of chairs and dining
tables create a comfortable atmosphere in the cafeteria.”)
- clean_area (“The cafeteria area is clean and well organized.”)
- clean_utensils (“The cleanliness of eating utensils (plates,
glasses, spoons, etc.) is maintained.”)
Product
Despite cleanliness aspect who had the lowest avg.score, there is
product aspect that has slight higher avg. score and
fell onto “Medium” category. As explained before, each aspect had its
own variables that fell into “Medium” category with a
room for improvement of product aspect.
Prioritized Variable for Product
- price_vs_quality (“The taste of the food/drinks in the cafeteria is
as expected.”)
- affordable_price (“The prices of food and drinks in the cafeteria
are affordable for students.”)
Services
At last, service aspect who had the highest avg. score. Despite being
the highest, this aspect also need a bit of change in one variable in
order to upgrade the quality of cafeteria services. The mentioned
variable is explained below:
Prioritized Variable for Services
- staff_response (“Canteen staff respond promptly when receiving
orders or complaints.”)
Main Recommendation
The cafeteria has weaknesses in terms of cleanliness and the product
it offers. In order to confront this challenges, some changes need to be
prioritized in both aspects who had some variables fell onto “Medium”
category. Some recommendations are offered to help the improvement of
canteen visitors satisfaction level, which will be explained
below:
- Standarize Cleanliness Facility
- Target: Cleanliness Aspect
- Reason Behind it: Cleanliness aspect has
the lowest total average score among the others, and it variables has
all variations surpassing 0.5. Especially in the cleaning_facility
variable, this shows differences in students experience between canteen
shifts.
- Product Quality and Price Control
- Target: Product Aspect
- Reason Behind it: Some variable is fell
into “Medium” category (e.g., price_vs_quality, affordable_price), which
leads to room for an improvement for an offered product. The analysis
results from variables such as price_vs_quality show that the
perception of value-for-money is still weak and
inconsistent among respondents.
- Equalizing Standard at Each Campus
- Target: All Aspect
- Reason Behind it: Segmentation analysis
has showed a significant differences in average scores (e.g., Binus
Semarang have the highest variations). Meanig that service and facility
standards need to be harmonized so that the student experience is more
consistent.
Steps for Implementations
Every plan need an execution steps to accomplish the changes for
improvement. This action plan may useful for some period of time such
as:
Short Term (1 - 3 month)
- Audit and Manual Monitoring
- Apply some checklist of daily cleaning inspections for tables,
chairs, and utensils.
- Add cleaning support staff during busy hours (e.g., lunch).
- Quick Roll Call
- Morning roll call and every shift change in private with cleaning
staff to ensure the hygiene standard & customer service are on
track.
- Product Adjustment
- Putting a QR codes next to food/snack shelf to obtain reviews
directly from consumers.
- Quick review for a new product in cafeteria, for example: give a
quick rate from 1 to 5 and short statements like “Food worth the
price.”, “The food is fresh when it gets to you.”, etc.)
Medium Term (3 - 6 month)
- Equalizing Standard at Every Campus
- Establish SOPs (Standard Operating Procedures) for applying QR codes
for quick reviews on new food shelves and encourage workers at morning
roll call and shift change times to remind customers to fill out reviews
every time they purchase new items. With this step, it’ll be saving much
more time to conducting an audit.
Long Term (6 - 12 month)
- Infrastructure Improvement
- Seeing the varied responses of students regarding hygiene
facilities, which indicate differences in facilities between cafeterias,
it is necessary to make improvements to the infrastructure. This is
important in order to provide comfort to students and ensure that they
experience the same facilities regardless of the cafeteria
location.
- Continuous evaluation
- Seeing the varied responses of students regarding hygiene
facilities, which indicate differences in facilities between cafeterias,
it is necessary to make improvements to the infrastructure. This is
important in order to provide comfort to students and ensure that they
experience the same facilities regardless of the cafeteria
location.
Closing: Expected impact
The implementation of the recommendations that have been formulated
is expected to improve the consistency of hygiene standards, product
quality, and uniformity of experience across campuses. With this step,
student satisfaction levels can increase significantly while
strengthening the image of the cafeteria as a facility that is
comfortable, hygienic, and meets student needs.